Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence

被引:10
作者
Doo, Florence X. [1 ,5 ]
Kulkarni, Pranav [1 ]
Siegel, Eliot L. [1 ,2 ]
Toland, Michael [3 ]
Yi, Paul H. [1 ]
Carlos, Ruth C. [4 ]
Parekh, Vishwa S. [1 ]
机构
[1] Univ Maryland, Med Intelligent Imaging UM2ii Ctr, Dept Radiol & Nucl Med, Baltimore, MD USA
[2] Univ Maryland, Baltimore, MD USA
[3] Univ Maryland Med Syst, Dept Diagnost Imaging & Nucl Med, Baltimore, MD USA
[4] Univ Michigan, Michigan Journal Amer Coll Radiol, Ann Arbor, MI USA
[5] 22 S Greene St, Baltimore, MD 21201 USA
关键词
Cloud; financial cost; environmental cost; artificial intelligence; large language models; TRENDS; CARE;
D O I
10.1016/j.jacr.2023.11.011
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technologies have become a cost-effective strategy by providing necessary infrastructure while reducing expenditures associated with hardware ownership, maintenance, and upgrades. Simultaneously, given the optimized energy consumption in modern cloud data centers, this transition is expected to reduce the environmental footprint of radiologic operations. The path to cloud integration comes with its own challenges, and radiology informatics leaders must consider elements such as cloud architectural choices, pricing, data security, uptime service agreements, user training and support, and broader interoperability. With the increasing importance of data-driven tools in radiology, understanding and navigating the cloud landscape will be essential for the future of radiology and its various stakeholders.
引用
收藏
页码:248 / 256
页数:9
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